Instructions to use Sag1012/machine-translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Sag1012/machine-translation with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Sag1012/machine-translation") - Notebooks
- Google Colab
- Kaggle
Download EncoderDecoder_7/model.safetensors from Sag1012/machine-translation: direct link, hf CLI and curl.
- Browser
- Download file 1.51 GB
-
https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_7/model.safetensors
- Command line
-
hf download hf://Sag1012/machine-translation/EncoderDecoder_7/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_7/model.safetensors
1.51 GB
- Xet hash:
- 4a5a286bb8c4f4e77907833538452ece3a68be0a73ed2fc4bacc567d093c02d4
- Size of remote file:
- 1.51 GB
- SHA256:
- 9891d963f0af4fe22444d7d9a3ccc62cc33e21ad5a15698b91f43008810c4123
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